Machine learning and AI are some of the biggest topics in the tech world right now, and Google is looking to make those fields more accessible to more people with its new Learn with Google AI website. Google has been pursuing AI education for a while, both with advanced projects like TensorFlow and more playful projects like cat doodles and a machine vision experiment meant to showcase AI projects in more practical ways. Google envisions the Learn with Google AI site serving as a repository for machine learning and AI, and it's meant to be a hub for anyone looking to "learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems." The site will apparently cater to all levels of AI enthusiasts, from researchers looking for advanced tutorials to beginners. The site also features a free course called Machine Learning Crash Course (MLCC).

Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.

Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.

How does machine learning differ from traditional programming? What is loss, and how do I measure it? How does gradient descent work? How do I determine whether my model is effective? How do I represent my data so that a program can learn from it? How do I build a deep neural network?

Some of us understand how Artificial Intelligence will impact manufacturing or R&D, but what about other areas of the business? For example, what role will it play in Learning & Development? What do leaders in L&D and HR need to consider in developing, using and promoting the use of AI products to their internal customers? Can it be used effectively to teach management skills? How is bias eliminated in such a program?

Vero, the controversial app that hopes to take down Instagram, now has more than a million users. The app has shot up the charts in recent days, amid excitement about its promise to avoid the problems of other apps like Facebook and Instagram. But it has run into problems, spending much of the time offline and attracting criticism over its terms and conditions. Vero had initially said that all users after the first million would have to pay a subscription fee to use the app. But it said that restriction is no longer in place "until further notice".

I'm sure you'll agree that Artificial Intelligence, in particular Deep Learning, has made huge strides in the last 5 years or so. But what began as a relatively niche field with just a handful of researchers, has now become so mainstream that the apps and services that we use everyday now use Deep Learning to perform tasks that were unthinkable not that long ago. It's been around since the 1940s when Warren McCulloch and Walter Pitts created a computational model for neural networks based on mathematics and algorithms. However "Deep Learning" only began to gain in popularity in the mid-2000s when Geoffrey Hinton and Ruslan Salakhutdinov released a paper showed how a multi-layered neural network could be pre-trained one layer at a time. In 2009 it was discovered that with large enough datasets, you didn't actually need the pre-training and that error rates could drop significantly as a result.

In part one of the machine learning crash course, we introduced the field of supervised machine learning (ML) by walking through popular algorithms like linear regression and logistic regression. But supervised learning is just one of the many types of algorithms in the vast machine learning / artificial intelligence space. In this article, we take a look at two other subdisciplines: Unsupervised learning and deep learning. When performing supervised learning, our datasets consisted of labeled examples. In the linear regression example, we had TV advertising data labeled with the amount of sales generated.

Artificial intelligence (A.I.) is making the "rise of machines" -- once the stuff of science fiction -- a reality. As 60 Minutes reported on October 9, "It might not be long before machines begin thinking for themselves -- creatively, independently, and sometimes with better judgment than a human." On February 28, 2018, you're invited to "Meet the Future" at DePauw University as the Ubben Lecture Series presents the world's first artificial intelligence-fueled android, Sophia, and her creator, David Hanson. In a 7:30 p.m. program in Kresge Auditorium, Dr. Hanson -- founder, CEO and chief designer of Hong Kong-based Hanson Robotics -- will be joined by his one-of-a-kind robot character. At the free event, which is open to all, the two will deliver a speech, take questions from the audience, and offer insights into the world of tomorrow that we're already entering today.

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.